{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "89f97991",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从sklearn.preprocessing里导入StandardScaler。\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# 从sklearn.linear_model里导入LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7d595dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X：每一项表示租金和面积\n",
    "# y：表示是否租赁该房间（0：不租，1：租）\n",
    "X=[[2200,15],[2750,20],[5000,40],[4000,20],[3300,20],[2000,10],[2500,12],[12000,80],\n",
    "   [2880,10],[2300,15],[1500,10],[3000,8],[2000,14],[2000,10],[2150,8],[3400,20],\n",
    "   [5000,20],[4000,10],[3300,15],[2000,12],[2500,14],[10000,100],[3150,10],\n",
    "   [2950,15],[1500,5],[3000,18],[8000,12],[2220,14],[6000,100],[3050,10]\n",
    "  ]\n",
    "\n",
    "y=[1,1,0,0,1,1,1,1,0,1,1,0,1,1,0,1,0,0,0,1,1,1,0,1,0,1,0,1,1,0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4487c89b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ss = StandardScaler()\n",
    "X_train = ss.fit_transform(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1e82be09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.60583897 -0.29313058]\n",
      " [-0.37682768 -0.09050576]\n",
      " [ 0.56003671  0.71999355]\n",
      " [ 0.14365254 -0.09050576]\n",
      " [-0.14781638 -0.09050576]\n",
      " [-0.68911581 -0.49575541]\n",
      " [-0.48092372 -0.41470548]\n",
      " [ 3.47472592  2.34099218]\n",
      " [-0.32269773 -0.49575541]\n",
      " [-0.56420055 -0.29313058]\n",
      " [-0.89730789 -0.49575541]\n",
      " [-0.27273163 -0.57680534]\n",
      " [-0.68911581 -0.33365555]\n",
      " [-0.68911581 -0.49575541]\n",
      " [-0.62665818 -0.57680534]\n",
      " [-0.10617796 -0.09050576]\n",
      " [ 0.56003671 -0.09050576]\n",
      " [ 0.14365254 -0.49575541]\n",
      " [-0.14781638 -0.29313058]\n",
      " [-0.68911581 -0.41470548]\n",
      " [-0.48092372 -0.33365555]\n",
      " [ 2.64195758  3.15149149]\n",
      " [-0.21027401 -0.49575541]\n",
      " [-0.29355084 -0.29313058]\n",
      " [-0.89730789 -0.69838024]\n",
      " [-0.27273163 -0.17155569]\n",
      " [ 1.80918923 -0.41470548]\n",
      " [-0.59751129 -0.33365555]\n",
      " [ 0.97642089  3.15149149]\n",
      " [-0.25191242 -0.49575541]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1dac0dbf",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#调用Lr中的fit模块训练模型参数\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "16bfc6b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "待预测的值： [[-0.68911581 -0.57680534]]\n",
      "predicted label =  [1]\n",
      "probability =  [[0.41886952 0.58113048]]\n"
     ]
    }
   ],
   "source": [
    "testX = [[2000,8]]\n",
    "X_test = ss.transform(testX)\n",
    "print(\"待预测的值：\",X_test)\n",
    "label = lr.predict(X_test)\n",
    "print(\"predicted label = \", label)\n",
    "#输出预测概率\n",
    "prob = lr.predict_proba(X_test)\n",
    "print(\"probability = \",prob)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69944b1a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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